Adoption Analytics System Using Value Drivers

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Solution Overview

Problem

Current customer adoption analytics in cloud-based software systems rely heavily on usage metrics that lack context, making it difficult to determine the effectiveness of feature adoption and usage, and thus hinder useful product development and strategic goal alignment.

Innovation Solution

A system that links customer value drivers to quantitative usage metrics, enabling automated monitoring, training, and installation of under-used product features by setting triggers based on adoption data, such as installation, enablement, and usage metrics, to improve feature adoption and usage efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If usage metrics are collected to measure feature adoption, then quantitative data is obtained, but the metrics lack context making them useless for determining effectiveness

Engineering Contradiction:

Improveusage metricsVSAvoidcontext information

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent introduces customer value drivers as an intermediary layer between usage metrics and analysis. Value drivers provide the contextual framework that connects raw usage data to business objectives, enabling meaningful interpretation of adoption effectiveness without losing the quantitative precision of the original metrics

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the analysis into multiple hierarchical levels: usage metrics at the operational level, value drivers at the strategic level, and their connections at the analytical level. This segmentation allows each component to maintain its specific function while collectively providing both precision and context

Inventive Principle:

Principle #1Segmentation

2Ease of operation

If automated training and enablement are provided for under-used features, then customer proficiency improves, but system complexity increases

Engineering Contradiction:

Improvecustomer proficiencyVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system automatically identifies under-used features through analytics and autonomously provides targeted training and enablement resources without requiring manual intervention. This self-service approach improves customer proficiency while minimizing the operational complexity burden on support teams

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements a feedback loop where usage metrics continuously inform automated training decisions, and the results of training are measured against value driver outcomes. This closed-loop feedback mechanism optimizes the training system's complexity by focusing resources only on features and customers where intervention is most needed

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11416879B1Adoption analytic based functionality

Publication Date: 2022.08.16 WORKDAY INC

AI summary

A system for triggering based on analytics comprises a storage device and a processor. The storage device is configured to store customer adoption data. A processor is configured to receive the customer adoption data; determine an under-used product feature based at least in part on the customer adoption data, wherein the customer adoption data is stored in the storage device; and determine and set a trigger for training the under-used product feature based at least in part on the customer adoption data.